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Containers vs Virtual Machines (VMs)

  • By Gcore
  • September 11, 2023
  • 12 min read
Containers vs Virtual Machines (VMs)

Virtualization allows multiple operating systems and applications to run on a single physical server, optimizing hardware resources and simplifying management. It comprises two interwoven technologies: containers and virtual machines. While containers are known for their speed, efficiency, and portability, VMs offer robust isolation and security features. This article explains and compares the technologies so you can understand their fit-for-purpose uses and make informed choices about your organization’s infrastructure needs.

Cliff Notes: What’s the Difference Between Containers and VMs?

In the realm of virtualization technologies, containers and virtual machines (VMs) offer distinct approaches to application isolation, resource utilization, and system architecture. Containers leverage OS-level virtualization for high performance and efficient resource sharing, whereas VMs operate on hardware-level virtualization, encapsulating a full guest OS for stronger isolation. These fundamental architectural differences result in varying levels of performance, startup time, and resource utilization.

Both technologies have their own merits and drawbacks when it comes to enterprise deployment, security protocols, and scalability. To understand the pros, cons, and use cases, we first need to look at each technology and its architecture in depth, and then at the end of the article we’ll offer a side-by-side comparison.

What Are Containers?

Containers are virtualized, isolated application packages; they contain everything needed to run a piece of software, including code, system tools, libraries, and settings (called dependencies.) They solve the critical software development and deployment problem of ensuring that software can run properly on different computing environments. This makes the software reliable for end users, an essential feature of any software release. To serve their purpose, containers are portable, meaning they can run across different development environments so that developers can test software on different environments without worrying about conflicts with the underlying system.

Benefits of Containers

Containers have been widely adopted for the following benefits:

  • Faster startup: Containers don’t need an operating system to start running. This means they can start almost instantly.
  • Efficient resource utilization: Containers utilize their host operating system’s kernel (the core part of the computer’s operating system.) This makes them lightweight and resource-efficient as they do not require the extra cost of a separate operating system installation for each container. This saves space and resources, helping things run fast and smoothly.
  • Portability: Containers encapsulate entire applications and their dependencies in a single file, allowing developers to build applications just once and then run them in multiple environments. This portability ensures consistent behavior when containers are moved between development, testing, and production environments, providing flexibility and reducing deployment-related issues.
  • Isolation: Multiple containers (and their dependencies) can share a single OS kernel, while running isolatedly in a way as to ensure that the failure of a container does not affect the function of others within the same environment. Dedicated security tools, such as OS security isolation tools, are also available to pinpoint faulty container parts for efficient remediation.
  • Scalability: You can run several similar containers to create multiple application instances simultaneously. Containerization also allows running only the containers needed for an application to function efficiently at a specific time. As such, containers can be scaled up or down depending on application load. These features make containers highly scalable and cost-effective.

How Do Containers Work?

Containers isolate applications and their dependencies into portable, self-contained units that can be operated anywhere. A container image—an immutable (unchangeable) file with executable code—creates a container. Container images are created using a Dockerfile (or similar configuration files) and stored in container repositories. When a container is instantiated, the image is pulled from the repository onto the host machine. After that, the image is verified, and any missing layers are fetched from the registry. Once the environment is set up, the runtime starts the container and runs the specific command defined in the image.

But What Is a Runtime?

A container runtime is a software package that leverages a container’s host OS to run the container. It creates the necessary environment for the execution of the container, including required namespaces and control groups (usually referred to as cgroups.) Both namespaces and cgroups facilitate resource efficiency: namespaces organize code into groups, and cgroups limit the utilization of resources. The container then runs within the isolated environment, utilizing the resources and namespaces provided by the host operating system.

Container Architecture

Containers have six major components: container runtimes, container images, registries, container orchestration platforms, control groups and namespaces. Let’s take a look at each in turn.

Container Runtimes

Container runtimes or engines are software components that are responsible for pulling container images from a registry, setting up the necessary execution environment, managing and executing containers on a host machine, and monitoring containers within the cluster environment. The container runtime also orchestrates the creation and execution of containers.

Container Images

Container images are lightweight, immutable snapshots that contain application codes, dependencies, configuration files, libraries and runtime environments required to run applications. Images have one or more layers built on a parent or base image. They are usually built using Dockerfiles, a text file specifying the instructions to create a layered image representing the application and its dependencies.

The layers of container images make their components and configurations reusable. So, developers do not have to create new ones from scratch whenever images are required. When built optimally, these layers can help to minimize container size and enhance performance.

Images (and other artifacts) make up repositories. Container images have names and tags for easy pushing and pulling. A repository and a tag define an image’s name and allow for easy sharing of container images. A single repository can contain several container images. To modify these read-only images, developers must create a layer—containing all changes made—on top of the parent image.

Relationship between the registry, image, and container

As shown in the above image, container images are stored in registries and can be pulled onto a host machine to create and run containers.

Registries

Registries are central repositories that store and distribute container images to ensure their efficiency. Registries can be public or private.

  • Public: This is a vast collection of prebuilt container images developers can use. DockerHub, a general catalog of container images, is an excellent example of a public container registry.
  • Private: This is a registry where organizations store and manage their own custom container images for privacy and greater control over them.

When an image is pulled from a registry, it is stored locally on the host machine to ensure that containers can be created without a network connection to the registry.

Container Orchestration Platforms

Container orchestration is an approach to automating and managing the deployment of containers to enhance application performance. Platforms—such as Kubernetes—are used to automate the deployment, scaling and scheduling of containers through several features, such as automated scaling, load balancing, and health monitoring.

The container registry, runtime and orchestration platform are three important components of the container orchestration setup required for managing containers throughout the software development life cycle.

Control Groups (cgroups)

The control group is one of the two main kernel OS features of containers, the other being namespaces (explained below.) cgroups provide the following functions:

  • Resource allocation: cgroups limit and prioritize resources (including CPU usage, memory, disk I/O, and network bandwidth) allocated to containers.
  • Process control: Because they can instantiate huge volumes of processes concurrently, containers are attack-prone. This is done to influence the volume of processes and deplete container resources. cgroups can be used to start, stop or restrict the number of ongoing processes, thereby preventing attacks and improving application performance.

Namespaces

Namespaces provide process-level isolation within the OS, ensuring that only the kernel OS gets shared; all other resources are isolated between containers. Namespaces enable containers to run with their own file system views, network stacks, process trees, and user privileges. Containers need their own system views, network stacks, process trees and user privileges to isolate themselves from other systems within the environment. For example, containers are typically given limited access to devices on the host, but with raised privileges, they can access the same capabilities as the host OS, ensuring container isolation.

Each container has its associated namespace that gives the illusion of individuality. There are four major namespaces used to allocate resources to containers:

NamespaceFunction
PID namespaceEnsures each container has its unique set of process IDs
Network namespaceAllows containers to have their own network interface, IP address, and routing table
Mount namespaceAllows containers to have their own root file system while avoiding conflicts with files from other containers
User namespaceProvides user and group isolation

The image below shows the relationship between the components of the container architecture explained above:

The container architecture

Use Cases for Containers

Containers have gained significant popularity due to their versatility and applicability across various industries. Let’s explore some of their notable use cases.

Microservices Security

Microservices are an architectural approach to software development and deployment, where applications comprise small independent and specialized services loosely connected via APIs (Application Programming Interfaces) and REST (representational state transfer) interfaces. Microservices deployed directly on a host OS are less secure because security vulnerabilities can easily spread in the OS layer. In contrast, containerized microservices are more secure and efficient, as containers reduce their attack surfaces, allow for independent patching, and offer control over resource allocation.

Application Packaging and Distribution

Since a container image hosts the application and its dependencies, it is easy to deploy across different environments. You only need to spin a YAML declaration of the container image and deploy it to any environment you choose. This eliminates the “works on my machine” problem often encountered with traditional deployments.

Continuous Integration and Continuous Deployment (CI/CD)

Developers leverage container images to facilitate CI/CD. CI is the practice of continuously making changes to software code and testing for its semantic correctness via an automated process. In-depth integration tests can be expensive, underscoring the value of containerization.

By encapsulating their applications into containers, developers can instantiate them from images throughout different stages of the development lifecycle. This approach, spanning from unit testing to staging and production, saves costs associated with setting up and running CI/CD servers while boosting efficiency.

Hybrid and Multicloud Environments

With containers, applications can be easily migrated across different cloud providers or on-premise infrastructure without extensive application reconfiguration.

Machine Learning, Edge Computing, and Internet of Things

This is another important use case of containers. Containers have increasingly been adopted in edge computing and internet of things (IoT) because they allow for lightweight deployment and enable different services to run on individual containers, thereby reducing the possibility of issues in one service affecting other services. Additionally, with the help of containerized environments, data scientists can ensure experiments are reproducible, easily share their work, and create consistent development environments for machine learning, artificial intelligence, and collaborative projects.

Now that we’ve covered containers, let’s turn to virtual machines.

What Are Virtual Machines?

A virtual machine (VM) is a virtualized emulation of an entire computer system within a host machine, including its hardware and operating system. It provides an environment that isolates the guest OS and applications from the underlying hardware and host operating system to prevent vulnerabilities of one component from affecting the entire cloud environment. A VM is built on a hypervisor, a software layer responsible for creating VMs and managing/allocating the physical resources of a host machine to virtualized guest systems based on their relative importance.

Benefits of Virtual Machines

Virtual machines offer several benefits:

  • Full isolation: A VM runs its own guest OS and application within its isolated environment in a way that ensures applications within one VM do not interfere with or impact applications in another VM.
  • Security: VMs provide strong security boundaries due to their full isolation feature. Compromised applications or vulnerabilities within one VM are generally contained within that specific VM and do not affect other VMs or the host system. This isolation helps protect sensitive data and mitigates the security risks associated with shared infrastructure.
  • OS flexibility: VMs allow different operating systems—including various versions of Windows, Linux and macOS—to be run on the same physical hardware. This flexibility enables organizations to run applications requiring specific operating systems or legacy software compatibility.
  • Resource partitioning: VMs enable the partitioning of physical resources, such as CPU, memory, and disks, among different virtual machines. This facilitates efficient workload management.
  • Snapshot capabilities: VMs offer the ability to take snapshots that capture the state of a VM at a specific point in time. These snapshots can be used for backup, disaster recovery, or testing.
  • Migration capabilities: VMs allow for seamless migration between host machines without service disruption, allowing IT teams to shift workloads or move between servers and machines. Migration is commonly done when VM host systems, including software and hardware, require patching or updating.
  • Hardware abstraction: VMs provide hardware abstraction by rendering physical hardware unnecessary, which helps to ensure software compatibility across different servers, as VMs shield applications from variations in hardware configurations. This helps to save costs associated with physical storage.

How Do Virtual Machines Work?

VMs work as fully isolated guest systems with their own operating systems and applications. They leverage the hypervisor to create and manage multiple isolated virtual environments on a single physical host. When a virtual machine is powered on, the hypervisor intercepts and translates the guest operating system’s instructions to the appropriate physical hardware.

This is enabled by hardware virtualization techniques, such as binary translation and hardware-assisted virtualization. Binary translation is a software virtualization technique in which an interpreter is used to translate the binary machine language code of an application into that of the host OS. Meanwhile, hardware-assisted virtualization involves using a computer’s physical components to provide the software architectural support that VMs need to function properly. Both techniques help to appropriately link and facilitate communication between VMs and the host OS.

Virtual Machine Architecture

VMs are built on physical machines, with their primary component being a hypervisor, a host server that allows VMs to access the required computing resources. The image below illustrates the four parts of the VM architecture.

VM architecture showing which components comprise the VM vs physical machine

Now, let’s detail these four components.

1. Virtual Machine (App, Bins/Libs, Guest OS)

Virtual machine components include virtualized applications and libraries presented to a guest OS. Each virtual machine has its own guest operating system, which can differ from each other, so that multiple operating systems can coexist on the same infrastructure.

2. Hypervisor

A hypervisor, also known as a virtual machine monitor (VMM,) is a software layer that enables the creation and management of VMs on the physical host machine/infrastructure. There are two types of hypervisors:

  • Type 1 (bare metal hypervisors) that run directly on the host machine’s hardware, without the need for an underlying OS. They have direct access to the hardware resources and manage the virtual machines at a low level.
  • Type 2 hypervisors run as applications on top of the existing OS. They rely on the host operating system to manage the hardware resources and provide a layer of virtualization on top of it.

3. Host Operating System

The host OS exists between the hypervisor and the infrastructure. They provide isolation between the virtual machine and the components of the infrastructure by limiting the data that flows from one to the other. This enhances both host and guest security.

4. Infrastructure

The infrastructure is the underlying hardware on which virtual machines are created and executed. It provides the necessary computing resources, such as CPU, memory, storage, and network.

How Are Virtual Machines Used?

Virtual machines are used across various industries and use cases, including the following:

Legacy Application Support

Virtual machines are commonly used to support and maintain legacy applications that were originally designed for an older OS or for specific hardware configurations. By running legacy applications on virtual machines, organizations can preserve the functionality and dependencies of these applications while modernizing their underlying infrastructure.

Multitenancy and Cloud Computing

VMs are used extensively in cloud environments where multiple customers share the same physical infrastructure. This is because they ensure cost-effectiveness and provide isolation, security, and customization while maximizing resource utilization for the cloud provider.

Development Environments

VMs provide convenient and isolated environments for developers to work on different projects with varying software requirements. Multiple VMs can be set up, each running a different development or testing stack, allowing for easy setup, experimentation, and reproducibility.

Testing and Debugging

VMs provide a controlled and reproducible environment for software testing and debugging. Testers can create multiple VM snapshots to capture various points in the software testing process, facilitating easy rollbacks, bug reproduction and debugging. Rollbacks involve reducing VMs to previous states to erase (or reduce) errors in current states, and bug reproduction is the process of reproducing a bug in a software to visualize how it got there in the first place. Both procedures facilitate debugging, patching and data retrieval. VMs offer a convenient way to test software compatibility across different OSes, reducing the need for multiple physical systems.

Security Research and Disaster Recovery

VMs are used for security research as well as vulnerability and malware analysis. Researchers can isolate and analyze potentially harmful applications or websites within VMs to prevent damage to host systems or networks.

What Are the Differences Between Containers and Virtual Machines (VMs)?

Although both are powerful and efficient, one has an edge over the other. As shown in the image below, the major architectural difference between both containers and VMs is the presence or absence of a guest operating system in the virtualization layer.

VMs vs Containers

Consider the table below for a close-range comparison:

FeaturesContainersVirtual Machines
ArchitectureApplication- or OS-level virtualization technology. Containers share the host OS kernel but have separate user spaces.Infrastructure or hardware-level virtualization technology. Each VM has its own kernel, file system, and memory allocation.
PerformancePerform better than VMs because they share the host operating system kernel. Lower overhead and improved performance.Reduced performance compared to containers, due to the overhead of running a full guest OS alongside the host OS.
Resource utilizationEfficient resource utilization due to leveraging their host’s resources directly.May be less efficient. Require dedicated resources for each virtual instance, including a separate guest OS.
LatencyLower latency as they directly access the host hardware, bypassing virtualization layers.Introduce additional layers of virtualization, leading to increased latency compared to containers.
Startup TimeOffer near-instant startup times.Longer startup times because they need to boot a complete guest OS for each instance.
IsolationUse OS-level isolation which is not as strong as hardware-level virtualization.Provide full isolation since each VM runs on a separate hypervisor.
PortabilityHighly portable, allowing for consistent behavior across different systems.Less portable as they may require configuration changes to work in different environments due to differences in hardware and OS versions.
SecurityPotential for security vulnerabilities due to the shared host OS kernel. Runtimes provide features like namespaces and control groups help to mitigate security risks.Higher level of security due to strong isolation between each VM.
FlexibilityGreater flexibility in terms of resource allocation and scaling.Limited flexibility; they require a fixed allocation of resources. Changes to resource allocation require adjusting VM configuration.
DensityDue to their lightweight nature, containers can be packed more densely on a host machine.Less dense due to resource overhead.
EfficiencyMore efficient in terms of storage and memory utilization.Require additional storage and memory, since each instance requires a full guest OS alongside the application.
Deployment complexitySimplified deployment process using container images.More complex deployment process, involving provisioning and configuring a guest OS on each virtual instance.
Use casesWell-suited for microservices architecture, continuous integration/continuous deployment, scaling applications, and managing distributed systems.Commonly used for running legacy applications, testing different operating systems, creating isolated OS environments, and development sandboxes.

Choosing Between Containers and Virtual Machines

Although both containers and VMS have revolutionized application development and deployment, your choice will depend on your applications’ specific use case, performance requirements, and isolation needs. Containers and VMs can be combined with containers running inside VMs to leverage the benefits of both technologies. To do this, create a virtual machine with a distinct hardware configuration and install an OS within it. Then, install a container runtime on the OS.

However, it is critical to note that containers lend themselves better to modern practices and use cases and offer portability that is important for multi-cloud environments. For instance, containers are more lightweight and use far fewer resources than virtual machines. If a physical server can host only ten virtual machines, it could host twenty containers or more.

Conclusion

Virtualization is undergoing a staggering rise in both popularity and market size, with its value currently estimated at $40–62 billion and expected to reach over $120 billion in revenue by 2027 according to Statista. As competition stiffens, selecting the right virtualization technology can give decision makers a competitive advantage over their counterparts.

Gcore’s Managed Kubernetes minimizes the complexity of using containers and empowers organizations to effortlessly orchestrate containerized applications, ensuring scalability, high availability, and simplified management, all without compromising security or performance.

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Migration costs and technical complexity often discourage switching providers.Limited customization: Cloud services offer standardized solutions that may not meet specific business requirements. Organizations can't modify underlying infrastructure or install custom software configurations. This restriction can force businesses to adapt their processes to fit the cloud platform's limitations.Ongoing costs: Monthly subscription fees can accumulate to exceed traditional on-premise infrastructure costs over time. Unexpected usage spikes or data transfer charges can lead to budget overruns. Organizations lose the asset value that comes with owning physical hardware.Performance variability: Shared cloud resources can experience slower performance during peak usage periods. Network latency affects applications requiring real-time processing or frequent data transfers. Organizations can't guarantee consistent performance levels for mission-critical applications.Compliance complexity: Meeting regulatory requirements becomes more challenging when data is stored across multiple locations. Organizations must verify that cloud providers meet industry-specific compliance standards. Audit trails and data governance become shared responsibilities that require careful coordination.Gcore Edge CloudWhen building AI applications that require serious computational power, the infrastructure you choose can make or break your project's success. Whether you're training large language models, running complex inference workloads, or tackling high-performance computing challenges, having access to the latest GPU technology without performance bottlenecks becomes critical.Gcore's AI GPU Cloud Infrastructure addresses these demanding requirements with bare metal NVIDIA H200. H100. A100. L40S, and GB200 GPUs, delivering zero virtualization overhead for maximum performance. The platform's ultra-fast InfiniBand networking and multi-GPU cluster support make it particularly well-suited for distributed training and large-scale AI workloads, starting from just €1.25/hour. Multi-instance GPU (MIG) support also allows you to improve resource allocation and costs for smaller inference tasks.Discover how Gcore's bare metal GPU performance can accelerate your AI training and inference workloads at https://gcore.com/gpu-cloud.Frequently asked questionsPeople often have questions about cloud computing basics, costs, and how it fits their specific needs. These answers cover the key service models, use options, and practical considerations that help clarify what cloud computing can do for your organization.What's the difference between cloud computing and traditional hosting?Cloud computing delivers resources over the internet on demand, while traditional hosting provides fixed server resources at dedicated locations. Cloud offers elastic growth and pay-as-you-go pricing, whereas traditional hosting requires upfront capacity planning and fixed costs regardless of actual usage.What is cloud computing security?Cloud computing security protects data, applications, and infrastructure in cloud environments through shared responsibility models between providers and users. Cloud providers secure the underlying infrastructure while users protect their data, applications, and access controls.What is virtualization in cloud computing?Virtualization in cloud computing creates multiple virtual machines (VMs) on a single physical server using hypervisor software that separates computing resources. This technology allows cloud providers to increase hardware effectiveness and offer flexible, isolated environments to multiple users simultaneously.Is cloud computing secure for business data?Yes, cloud computing is secure for business data when proper security measures are in place, with major providers offering encryption, access controls, and compliance certifications that often exceed what most businesses can achieve on-premises. Cloud service providers typically guarantee 99.9% or higher uptime in service level agreements while maintaining enterprise-grade security standards.How much does cloud computing cost compared to on-premises infrastructure?Cloud computing typically costs 20-40% less than on-premises infrastructure due to shared resources, reduced hardware purchases, and lower maintenance expenses, according to IDC (2024). However, costs vary primarily based on usage patterns, with predictable workloads sometimes being cheaper on-premises while variable workloads benefit more from cloud's pay-as-you-go model.How do I choose between IaaS, PaaS, and SaaS?Choose based on your control needs. IaaS gives you full infrastructure control, PaaS handles infrastructure so you focus on development, and SaaS provides ready-to-use applications with no technical management required.

Pre-configure your dev environment with Gcore VM init scripts

Provisioning new cloud instances can be repetitive and time-consuming if you’re doing everything manually: installing packages, configuring environments, copying SSH keys, and more. With cloud-init, you can automate these tasks and launch development-ready instances from the start.Gcore Edge Cloud VMs support cloud-init out of the box. With a simple YAML script, you can automatically set up a development-ready instance at boot, whether you’re launching a single machine or spinning up a fleet.In this guide, we’ll walk through how to use cloud-init on Gcore Edge Cloud to:Set a passwordInstall packages and system updatesAdd users and SSH keysMount disks and write filesRegister services or install tooling like Docker or Node.jsLet’s get started.What is cloud-init?cloud-init is a widely used tool for customizing cloud instances during the first boot. It reads user-provided configuration data—usually YAML—and uses it to run commands, install packages, and configure the system. In this article, we will focus on Linux-based virtual machines.How to use cloud-init on GcoreFor Gcore Cloud VMs, cloud-init scripts are added during instance creation using the User data field in the UI or API.Step 1: Create a basic scriptStart with a simple YAML script. Here’s one that updates packages and installs htop:#cloud-config package_update: true packages: - htop Step 2: Launch a new VM with your scriptGo to the Gcore Customer Portal, navigate to VMs, and start creating a new instance (or just click here). When you reach the Additional options section, enable the User data option. Then, paste in your YAML cloud-init script.Once the VM boots, it will automatically run the script. This works the same way for all supported Linux distributions available through Gcore.3 real-world examplesLet’s look at three examples of how you can use this.Example 1: Add a password for a specific userThe below script sets the for the default user of the selected operating system:#cloud-config password: <password> chpasswd: {expire: False} ssh_pwauth: True Example 2: Dev environment with Docker and GitThe following script does the following:Installs Docker and GitAdds a new user devuser with sudo privilegesAuthorizes an SSH keyStarts Docker at boot#cloud-config package_update: true packages: - docker.io - git users: - default - name: devuser sudo: ALL=(ALL) NOPASSWD:ALL groups: docker shell: /bin/bash ssh-authorized-keys: - ssh-rsa AAAAB3Nza...your-key-here runcmd: - systemctl enable docker - systemctl start docker Example 3: Install Node.js and clone a repoThis script installs Node.js and clones a GitHub repo to your Gcore VM at launch:#cloud-config packages: - curl runcmd: - curl -fsSL https://deb.nodesource.com/setup_18.x | bash - - apt-get install -y nodejs - git clone https://github.com/example-user/dev-project.git /home/devuser/project Reusing and versioning your scriptsTo avoid reinventing the wheel, keep your cloud-init scripts:In version control (e.g., Git)Templated for different environments (e.g., dev vs staging)Modular so you can reuse base blocks across projectsYou can also use tools like Ansible or Terraform with cloud-init blocks to standardize provisioning across your team or multiple Gcore VM environments.Debugging cloud-initIf your script doesn’t behave as expected, SSH into the instance and check the cloud-init logs:sudo cat /var/log/cloud-init-output.log This file shows each command as it ran and any errors that occurred.Other helpful logs:/var/log/cloud-init.log /var/lib/cloud/instance/user-data.txt Pro tip: Echo commands or write log files in your script to help debug tricky setups—especially useful if you’re automating multi-node workflows across Gcore Cloud.Tips and best practicesIndentation matters! YAML is picky. Use spaces, not tabs.Always start the file with #cloud-config.runcmd is for commands that run at the end of boot.Use write_files to write configs, env variables, or secrets.Cloud-init scripts only run on the first boot. To re-run, you’ll need to manually trigger cloud-init or re-create the VM.Automate it all with GcoreIf you're provisioning manually, you're doing it wrong. Cloud-init lets you treat your VM setup as code: portable, repeatable, and testable. Whether you’re spinning up ephemeral dev boxes or preparing staging environments, Gcore’s support for cloud-init means you can automate it all.For more on managing virtual machines with Gcore, check out our product documentation.Explore Gcore VM product docs

How to cut egress costs and speed up delivery using Gcore CDN and Object Storage

If you’re serving static assets (images, videos, scripts, downloads) from object storage, you’re probably paying more than you need to, and your users may be waiting longer than they should.In this guide, we explain how to front your bucket with Gcore CDN to cache static assets, cut egress bandwidth costs, and get faster TTFB globally. We’ll walk through setup (public or private buckets), signed URL support, cache control best practices, debugging tips, and automation with the Gcore API or Terraform.Why bother?Serving directly from object storage hits your origin for every request and racks up egress charges. With a CDN in front, cached files are served from edge—faster for users, and cheaper for you.Lower TTFB, better UXWhen content is cached at the edge, it doesn’t have to travel across the planet to get to your user. Gcore CDN caches your assets at PoPs close to end users, so requests don’t hit origin unless necessary. Once cached, assets are delivered in a few milliseconds.Lower billsMost object storage providers charge $80–$120 per TB in egress fees. By fronting your storage with a CDN, you only pay egress once per edge location—then it’s all cache hits after that. If you’re using Gcore Storage and Gcore CDN, there’s zero egress fee between the two.Caching isn’t the only way you save. Gcore CDN can also compress eligible file types (like HTML, CSS, JavaScript, and JSON) on the fly, further shrinking bandwidth usage and speeding up file delivery—all without any changes to your storage setup.Less origin traffic and less data to transfer means smaller bills. And your storage bucket doesn’t get slammed under load during traffic spikes.Simple scaling, globallyThe CDN takes the hit, not your bucket. That means fewer rate-limit issues, smoother traffic spikes, and more reliable performance globally. Gcore CDN spans the globe, so you’re good whether your users are in Tokyo, Toronto, or Tel Aviv.Setup guide: Gcore CDN + Gcore Object StorageLet’s walk through configuring Gcore CDN to cache content from a storage bucket. This works with Gcore Object Storage and other S3-compatible services.Step 1: Prep your bucketPublic? Check files are publicly readable (via ACL or bucket policy).Private? Use Gcore’s AWS Signature V4 support—have your access key, secret, region, and bucket name ready.Gcore Object Storage URL format: https://<bucket-name>.<region>.cloud.gcore.lu/<object> Step 2: Create CDN resource (UI or API)In the Gcore Customer Portal:Go to CDN > Create CDN ResourceChoose "Accelerate and protect static assets"Set a CNAME (e.g. cdn.yoursite.com) if you want to use your domainConfigure origin:Public bucket: Choose None for authPrivate bucket: Choose AWS Signature V4, and enter credentialsChoose HTTPS as the origin protocolGcore will assign a *.gcdn.co domain. If you’re using a custom domain, add a CNAME: cdn.yoursite.com CNAME .gcdn.co Here’s how it works via Terraform: resource "gcore_cdn_resource" "cdn" { cname = "cdn.yoursite.com" origin_group_id = gcore_cdn_origingroup.origin.id origin_protocol = "HTTPS" } resource "gcore_cdn_origingroup" "origin" { name = "my-origin-group" origin { source = "mybucket.eu-west.cloud.gcore.lu" enabled = true } } Step 3: Set caching behaviorSet Cache-Control headers in your object metadata: Cache-Control: public, max-age=2592000 Too messy to handle in storage? Override cache logic in Gcore:Force TTLs by path or extensionIgnore or forward query strings in cache keyStrip cookies (if unnecessary for cache decisions)Pro tip: Use versioned file paths (/img/logo.v3.png) to bust cache safely.Secure access with signed URLsWant your assets to be private, but still edge-cacheable? Use Gcore’s Secure Token feature:Enable Secure Token in CDN settingsSet a secret keyGenerate time-limited tokens in your appPython example: import base64, hashlib, time secret = 'your_secret' path = '/videos/demo.mp4' expires = int(time.time()) + 3600 string = f"{expires}{path} {secret}" token = base64.urlsafe_b64encode(hashlib.md5(string.encode()).digest()).decode().strip('=') url = f"https://cdn.yoursite.com{path}?md5={token}&expires={expires}" Signed URLs are verified at the CDN edge. Invalid or expired? Blocked before origin is touched.Optional: Bind the token to an IP to prevent link sharing.Debug and cache tuneUse curl or browser devtools: curl -I https://cdn.yoursite.com/img/logo.png Look for:Cache: HIT or MISSCache-ControlX-Cached-SinceCache not working? Check for the following errors:Origin doesn’t return Cache-ControlCDN override TTL not appliedCache key includes query strings unintentionallyYou can trigger purges from the Gcore Customer Portal or automate them via the API using POST /cdn/purge. Choose one of three ways:Purge all: Clear the entire domain’s cache at once.Purge by URL: Target a specific full path (e.g., /images/logo.png).Purge by pattern: Target a set of files using a wildcard at the end of the pattern (e.g., /videos/*).Monitor and optimize at scaleAfter rollout:Watch origin bandwidth dropCheck hit ratio (aim for >90%)Audit latency (TTFB on HIT vs MISS)Consider logging using Gcore’s CDN logs uploader to analyze cache behavior, top requested paths, or cache churn rates.For maximum savings, combine Gcore Object Storage with Gcore CDN: egress traffic between them is 100% free. That means you can serve cached assets globally without paying a cent in bandwidth fees.Using external storage? You’ll still slash egress costs by caching at the edge and cutting direct origin traffic—but you’ll unlock the biggest savings when you stay inside the Gcore ecosystem.Save money and boost performance with GcoreStill serving assets direct from storage? You’re probably wasting money and compromising performance on the table. Front your bucket with Gcore CDN. Set smart cache headers or use overrides. Enable signed URLs if you need control. Monitor cache HITs and purge when needed. Automate the setup with Terraform. Done.Next steps:Create your CDN resourceUse private object storage with Signature V4Secure your CDN with signed URLsCreate a free CDN resource now

Bare metal vs. virtual machines: performance, cost, and use case comparison

Choosing the right type of server infrastructure is critical to how your application performs, scales, and fits your budget. For most workloads, the decision comes down to two core options: bare metal servers and cloud virtual machines (VMs). Both can be deployed in the cloud, but they differ significantly in terms of performance, control, scalability, and cost.In this article, we break down the core differences between bare metal and virtual servers, highlight when to choose each, and explain how Gcore can help you deploy the right infrastructure for your needs. If you want to learn about either BM or VMs in detail, we’ve got articles for those: here’s the one for bare metal, and here’s a deep dive into virtual machines.Bare metal vs. virtual machines at a glanceWhen evaluating whether bare metal or virtual machines are right for your company, consider your specific workload requirements, performance priorities, and business objectives. Here’s a quick breakdown to help you decide what works best for you.FactorBare metal serversVirtual machinesPerformanceDedicated resources; ideal for high-performance workloadsShared resources; suitable for moderate or variable workloadsScalabilityOften requires manual scaling; less flexibleHighly elastic; easy to scale up or downCustomizationFull control over hardware, OS, and configurationLimited by hypervisor and provider’s environmentSecurityIsolated by default; no hypervisor layerShared environment with strong isolation protocolsCostHigher upfront cost; dedicated hardwarePay-as-you-go pricing; cost-effective for flexible workloadsBest forHPC, AI/ML, compliance-heavy workloadsStartups, dev/test, fast-scaling applicationsAll about bare metal serversA bare metal server is a single-tenant physical server rented from a cloud provider. Unlike virtual servers, the hardware is not shared with other users, giving you full access to all resources and deeper control over configurations. You get exclusive access and control over the hardware via the cloud provider, which offers the stability and security needed for high-demand applications.The benefits of bare metal serversHere are some of the business advantages of opting for a bare metal server:Maximized performance: Because they are dedicated resources, bare metal servers provide top-tier performance without sharing processing power, memory, or storage with other users. This makes them ideal for resource-intensive applications like high-performance computing (HPC), big data processing, and game hosting.Greater control: Since you have direct access to the hardware, you can customize the server to meet your specific requirements. This is especially important for businesses with complex, specialized needs that require fine-tuned configurations.High security: Bare metal servers offer a higher level of security than their alternatives due to the absence of virtualization. With no shared resources or hypervisor layer, there’s less risk of vulnerabilities that come with multi-tenant environments.Dedicated resources: Because you aren’t sharing the server with other users, all server resources are dedicated to your application so that you consistently get the performance you need.Who should use bare metal servers?Here are examples of instances where bare metal servers are the best option for a business:High-performance computing (HPC)Big data processing and analyticsResource-intensive applications, such as AI/ML workloadsGame and video streaming serversBusinesses requiring enhanced security and complianceAll about virtual machinesA virtual server (or virtual machine) runs on top of a physical server that’s been partitioned by a cloud provider using a hypervisor. This allows multiple VMs to share the same hardware while remaining isolated from each other.Unlike bare metal servers, virtual machines share the underlying hardware with other cloud provider customers. That means you’re using (and paying for) part of one server, providing cost efficiency and flexibility.The benefits of virtual machinesHere are some advantages of using a shared virtual machine:Scalability: Virtual machines are ideal for businesses that need to scale quickly and are starting at a small scale. With cloud-based virtualization, you can adjust your server resources (CPU, memory, storage) on demand to match changing workloads.Cost efficiency: You pay only for the resources you use with VMs, making them cost-effective for companies with fluctuating resource needs, as there is no need to pay for unused capacity.Faster deployment: VMs can be provisioned quickly and easily, which makes them ideal for anyone who wants to deploy new services or applications fast.Who should use virtual machines?VMs are a great fit for the following:Web hosting and application hostingDevelopment and testing environmentsRunning multiple apps with varying demandsStartups and growing businesses requiring scalabilityBusinesses seeking cost-effective, flexible solutionsWhich should you choose?There’s no one-size-fits-all answer. Your choice should depend on the needs of your workload:Choose bare metal if you need dedicated performance, low-latency access to hardware, or tighter control over security and compliance.Choose virtual servers if your priority is flexible scaling, faster deployment, and optimized cost.If your application uses GPU-based inference or AI training, check out our dedicated guide to VM vs. BM for AI workloads.Get started with Gcore BM or VMs todayAt Gcore, we provide both bare metal and virtual machine solutions, offering flexibility, performance, and reliability to meet your business needs. Gcore Bare Metal has the power and reliability needed for demanding workloads, while online virtual machines offers customizable configurations, free egress traffic, and flexibility.Compare Gcore BM and VM pricing now

Optimize your workload: a guide to selecting the best virtual machine configuration

Virtual machines (VMs) offer the flexibility, scalability, and cost-efficiency that businesses need to optimize workloads. However, choosing the wrong setup can lead to poor performance, wasted resources, and unnecessary costs.In this guide, we’ll walk you through the essential factors to consider when selecting the best virtual machine configuration for your specific workload needs.﹟1 Understand your workload requirementsThe first step in choosing the right virtual machine configuration is understanding the nature of your workload. Workloads can range from light, everyday tasks to resource-intensive applications. When making your decision, consider the following:Compute-intensive workloads: Applications like video rendering, scientific simulations, and data analysis require a higher number of CPU cores. Opt for VMs with multiple processors or CPUs for smoother performance.Memory-intensive workloads: Databases, big data analytics, and high-performance computing (HPC) jobs often need more RAM. Choose a VM configuration that provides sufficient memory to avoid memory bottlenecks.Storage-intensive workloads: If your workload relies heavily on storage, such as file servers or applications requiring frequent read/write operations, prioritize VM configurations that offer high-speed storage options, such as SSDs or NVMe.I/O-intensive workloads: Applications that require frequent network or disk I/O, such as cloud services and distributed applications, benefit from VMs with high-bandwidth and low-latency network interfaces.﹟2 Consider VM size and scalabilityOnce you understand your workload’s requirements, the next step is to choose the right VM size. VM sizes are typically categorized by the amount of CPU, memory, and storage they offer.Start with a baseline: Select a VM configuration that offers a balanced ratio of CPU, RAM, and storage based on your workload type.Scalability: Choose a VM size that allows you to easily scale up or down as your needs change. Many cloud providers offer auto-scaling capabilities that adjust your VM’s resources based on real-time demand, providing flexibility and cost savings.Overprovisioning vs. underprovisioning: Avoid overprovisioning (allocating excessive resources) unless your workload demands peak capacity at all times, as this can lead to unnecessary costs. Similarly, underprovisioning can affect performance, so finding the right balance is essential.﹟3 Evaluate CPU and memory considerationsThe central processing unit (CPU) and memory (RAM) are the heart of a virtual machine. The configuration of both plays a significant role in performance. Workloads that need high processing power, such as video encoding, machine learning, or simulations, will benefit from VMs with multiple CPU cores. However, be mindful of CPU architecture—look for VMs that offer the latest processors (e.g., Intel Xeon, AMD EPYC) for better performance per core.It’s also important that the VM has enough memory to avoid paging, which occurs when the system uses disk space as virtual memory, significantly slowing down performance. Consider a configuration with more RAM and support for faster memory types like DDR4 for memory-heavy applications.﹟4 Assess storage performance and capacityStorage performance and capacity can significantly impact the performance of your virtual machine, especially for applications requiring large data volumes. Key considerations include:Disk type: For faster read/write operations, opt for solid-state drives (SSDs) over traditional hard disk drives (HDDs). Some cloud providers also offer NVMe storage, which can provide even greater speed for highly demanding workloads.Disk size: Choose the right size based on the amount of data you need to store and process. Over-allocating storage space might seem like a safe bet, but it can also increase costs unnecessarily. You can always resize disks later, so avoid over-allocating them upfront.IOPS and throughput: Some workloads require high input/output operations per second (IOPS). If this is a priority for your workload (e.g., databases), make sure that your VM configuration includes high IOPS storage options.﹟5 Weigh up your network requirementsWhen working with cloud-based VMs, network performance is a critical consideration. High-speed and low-latency networking can make a difference for applications such as online gaming, video conferencing, and real-time analytics.Bandwidth: Check whether the VM configuration offers the necessary bandwidth for your workload. For applications that handle large data transfers, such as cloud backup or file servers, make sure that the network interface provides high throughput.Network latency: Low latency is crucial for applications where real-time performance is key (e.g., trading systems, gaming). Choose VMs with low-latency networking options to minimize delays and improve the user experience.Network isolation and security: Check if your VM configuration provides the necessary network isolation and security features, especially when handling sensitive data or operating in multi-tenant environments.﹟6 Factor in cost considerationsWhile it’s essential that your VM has the right configuration, cost is always an important factor to consider. Cloud providers typically charge based on the resources allocated, so optimizing for cost efficiency can significantly impact your budget.Consider whether a pay-as-you-go or reserved model (which offers discounted rates in exchange for a long-term commitment) fits your usage pattern. The reserved option can provide significant savings if your workload runs continuously. You can also use monitoring tools to track your VM’s performance and resource usage over time. This data will help you make informed decisions about scaling up or down so you’re not paying for unused resources.﹟7 Evaluate security featuresSecurity is a primary concern when selecting a VM configuration, especially for workloads handling sensitive data. Consider the following:Built-in security: Look for VMs that offer integrated security features such as DDoS protection, WAAP security, and encryption.Compliance: Check that the VM configuration meets industry standards and regulations, such as GDPR, ISO 27001, and PCI DSS.Network security: Evaluate the VM's network isolation capabilities and the availability of cloud firewalls to manage incoming and outgoing traffic.﹟8 Consider geographic locationThe geographic location of your VM can impact latency and compliance. Therefore, it’s a good idea to choose VM locations that are geographically close to your end users to minimize latency and improve performance. In addition, it’s essential to select VM locations that comply with local data sovereignty laws and regulations.﹟9 Assess backup and recovery optionsBackup and recovery are critical for maintaining data integrity and availability. Look for VMs that offer automated backup solutions so that data is regularly saved. You should also evaluate disaster recovery capabilities, including the ability to quickly restore data and applications in case of failure.﹟10 Test and iterateFinally, once you've chosen a VM configuration, testing its performance under real-world conditions is essential. Most cloud providers offer performance monitoring tools that allow you to assess how well your VM is meeting your workload requirements.If you notice any performance bottlenecks, be prepared to adjust the configuration. This could involve increasing CPU cores, adding more memory, or upgrading storage. Regular testing and fine-tuning means that your VM is always optimized.Choosing a virtual machine that suits your requirementsSelecting the best virtual machine configuration is a key step toward optimizing your workloads efficiently, cost-effectively, and without unnecessary performance bottlenecks. By understanding your workload’s needs, considering factors like CPU, memory, storage, and network performance, and continuously monitoring resource usage, you can make informed decisions that lead to better outcomes and savings.Whether you're running a small application or large-scale enterprise software, the right VM configuration can significantly improve performance and cost. Gcore provides flexible online virtual machine options that can meet your unique requirements. Our virtual machines are designed to meet diverse workload requirements, providing dedicated vCPUs, high-speed storage, and low-latency networking across 30+ global regions. You can scale compute resources on demand, benefit from free egress traffic, and enjoy flexible pricing models by paying only for the resources in use, maximizing the value of your cloud investments.Contact us to discuss your VM needs

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